Softwood Lumber's "Termite" Problem: Why the Extension of the 2006 Softwood Lumber Agreement Is Right for Softwood Lumber but Wrong for the Multilateral Trading System
Bibliographic record
Abstract
SOFTWOOD LUMBER'S "TERMITE" PROBLEM 167countries trading over $597 billion during 2011 alone.12 More specifically, Canada has historically been the largest source of lumber imports in the United States.13 Culminating with the SLA 2006, the most recent developments in the softwood lumber dispute resulted from what commentators have labeled a "hydra" of litigation.14 Disputes were settled under Chapters 11 and 19 of the North American Free Trade Agreement (NAFTA), the World Trade Organization (WTO) dispute settlement system, and within U.S. domestic courts.15 This overlap is a result of the proliferation of preferential trade agreements (PTAs) 16 within the international trading system, which enable parties to seek recourse under multiple dispute settlement regimes. 17Although NAFTA contains a choice of forum clause intended to prevent overlap in dispute settlement, 18 the clause does not apply to final AD or CVD determinations, such as those at issue in the 12 Top Trading Partners, U.S. CENSUS BUREAU, http://www.census.gov/foreign-trade/statistics/highlights/top/top1112yr.html(last visited Dec. 19, 2012).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".